Fast hog feature computation based on CUDA

Histogram of oriented gradients (HOG) is one of the most popular descriptors used for pedestrian detection, but this descriptor has its own drawback. Like most sliding window algorithms it is very slow, making it unsuitable for many real-time applications. This paper proposes a parallel implementati...

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Veröffentlicht in:2011 IEEE International Conference on Computer Science and Automation Engineering Jg. 4; S. 748 - 751
Hauptverfasser: Chen Yan-ping, Li Shao-zi, Lin Xian-ming
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2011
Schlagworte:
CUDA
GPU
HOG
pedestrian detection
ISBN:9781424487271, 1424487277
Online-Zugang:Volltext
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Zusammenfassung:Histogram of oriented gradients (HOG) is one of the most popular descriptors used for pedestrian detection, but this descriptor has its own drawback. Like most sliding window algorithms it is very slow, making it unsuitable for many real-time applications. This paper proposes a parallel implementation of the HOG algorithm. It bases on CUDA (compute unified device architecture) platform that could use parallel computing of graphic processing unit (GPU). The time consumption of HOG running on the GPU and on the CPU is compared by experiments in this paper. The results demonstrate that the HOG on GPU performs better than the HOG running on CPU, and is approximate 10 times speedup.
ISBN:9781424487271
1424487277
DOI:10.1109/CSAE.2011.5952952